1. Field of the Invention
The present invention relates to techniques for reducing uncertainty in a quantized signal. More specifically, the present invention relates to a method and apparatus for reducing uncertainty in a quantized signal by margining the quantized signal.
2. Related Art
Modern computer components such as CPU boards and I/O boards incorporate onboard monitoring mechanisms to protect the electrical components from over-voltage, over-current, and over-temperature events, and to also prevent such events from damaging the rest of the computer system. These monitoring mechanisms typically generate analog electrical signals, which must be converted into corresponding digital signals using analog-to-digital (A/D) converters prior to analyzing the signals. A/D converters often have coarse resolution, and therefore, some signals may be represented by only one or two different values during normal operation. Unfortunately, this low resolution does not make it possible to provide accurate surveillance of the statistical characteristics of the signals for high sensitivity anomaly detection and system failure prevention.
During the system design phase, if a decision is made to use a low-resolution A/D converter, the resulting digitized signal can be severely quantized. For example, commonly used 8-bit A/D converters are only able to encode 256 discrete analog values across the range of the signal. Moreover, all signal values within a given interval (which is referred to as a “quantization interval”) are represented as a single digital value.
FIG. 1 illustrates an exemplary signal which is severely quantized. More specifically, FIG. 1 illustrates signal 102, quantized signal 104, and uncertainty intervals 106, 108, 110, and 112. (In FIG. 1, signal 102 is offset from quantized signal 104 to show the two separate signals more clearly.) Note that the spread or variation of signal 102 is much less than the uncertainty intervals, and furthermore, the A/D converter reports quantized signal 104 as a flat line within each uncertainty interval. Therefore, it is statistically impossible to reconstruct the mean and the variance of the original signal from this quantized signal because the true value of the signal can be anywhere within the quantization intervals.
Severe quantization may not be a problem for certain diagnostic applications. However, it is desirable to detect developing anomalies, such as a slow drift in the mean or the variance in a signal, as early as possible. Note that a small drift in either the mean or the variance of a signal can indicate the onset of hardware degradation. Moreover, early detection of these anomalies can help identify the onset of hardware degradation and prevent system failure.
Hence, what is needed is a method and an apparatus for reducing the uncertainty in a severely quantized signal to improve diagnostic sensitivity without the problems described above.